{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Time Series Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have limited our modeling efforts to single time series. RNNs are naturally well suited to multivariate time series and represent a non-linear alternative to the Vector Autoregressive (VAR) models we covered in Chapter 8, Time Series Models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:49:00.215301Z",
     "start_time": "2020-06-22T18:49:00.211983Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:49:02.701665Z",
     "start_time": "2020-06-22T18:49:00.744455Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:49:13.283201Z",
     "start_time": "2020-06-22T18:49:13.255144Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:49:13.545013Z",
     "start_time": "2020-06-22T18:49:13.541545Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:49:13.896942Z",
     "start_time": "2020-06-22T18:49:13.893864Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'multivariate_time_series')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, we illustrate the application of RNNs to modeling and forecasting several time series using the same dataset we used for the VAR example, monthly data on consumer sentiment, and industrial production from the Federal Reserve's FRED service in Chapter 8, Time Series Models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:00.127185Z",
     "start_time": "2020-06-22T00:51:59.750241Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 480 entries, 1980-01-01 to 2019-12-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   sentiment  480 non-null    float64\n",
      " 1   ip         480 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 11.2 KB\n"
     ]
    }
   ],
   "source": [
    "df = web.DataReader(['UMCSENT', 'IPGMFN'], 'fred', '1980', '2019-12').dropna()\n",
    "df.columns = ['sentiment', 'ip']\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:00.138725Z",
     "start_time": "2020-06-22T00:52:00.129032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>67.0</td>\n",
       "      <td>46.8770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-02-01</th>\n",
       "      <td>66.9</td>\n",
       "      <td>47.9757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-01</th>\n",
       "      <td>56.5</td>\n",
       "      <td>48.4793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>52.7</td>\n",
       "      <td>47.0662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-05-01</th>\n",
       "      <td>51.7</td>\n",
       "      <td>45.6995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sentiment       ip\n",
       "DATE                          \n",
       "1980-01-01       67.0  46.8770\n",
       "1980-02-01       66.9  47.9757\n",
       "1980-03-01       56.5  48.4793\n",
       "1980-04-01       52.7  47.0662\n",
       "1980-05-01       51.7  45.6995"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stationarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the same transformation—annual difference for both series, prior log-transform for industrial production—to achieve stationarity that we used in Chapter 8 on Time Series Models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:00.149779Z",
     "start_time": "2020-06-22T00:52:00.141125Z"
    }
   },
   "outputs": [],
   "source": [
    "df_transformed = (pd.DataFrame({'ip': np.log(df.ip).diff(12),\n",
    "                                'sentiment': df.sentiment.diff(12)})\n",
    "                  .dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we scale the transformed data to the [0,1] interval:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:00.158350Z",
     "start_time": "2020-06-22T00:52:00.152640Z"
    }
   },
   "outputs": [],
   "source": [
    "df_transformed = df_transformed.apply(minmax_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot original and transformed series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.764099Z",
     "start_time": "2020-06-22T00:52:00.159795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(14,4))\n",
    "columns={'ip': 'Industrial Production', 'sentiment': 'Sentiment'}\n",
    "df.rename(columns=columns).plot(ax=axes[0], title='Original Series')\n",
    "df_transformed.rename(columns=columns).plot(ax=axes[1], title='Tansformed Series')\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'multi_rnn', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshape data into RNN format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reshape directly to get non-overlapping series, i.e., one sample for each year (works only if the number of samples is divisible by window size):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.768553Z",
     "start_time": "2020-06-22T00:52:01.765217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 12, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.reshape(-1, 12, 2).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we want rolling, not non-overlapping lagged values. The create_multivariate_rnn_data function transforms a dataset of several time series into the shape required by the Keras RNN layers, namely `n_samples` x `window_size` x `n_series`, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.782897Z",
     "start_time": "2020-06-22T00:52:01.769964Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_multivariate_rnn_data(data, window_size):\n",
    "    y = data[window_size:]\n",
    "    n = data.shape[0]\n",
    "    X = np.stack([data[i: j] \n",
    "                  for i, j in enumerate(range(window_size, n))], axis=0)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use window_size of 24 months and obtain the desired inputs for our RNN model, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.791243Z",
     "start_time": "2020-06-22T00:52:01.784331Z"
    }
   },
   "outputs": [],
   "source": [
    "window_size = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.874967Z",
     "start_time": "2020-06-22T00:52:01.792943Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = create_multivariate_rnn_data(df_transformed, window_size=window_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.879650Z",
     "start_time": "2020-06-22T00:52:01.876378Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((450, 18, 2), (450, 2))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.893794Z",
     "start_time": "2020-06-22T00:52:01.880888Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ip</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>0.526669</td>\n",
       "      <td>0.576214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-01</th>\n",
       "      <td>0.513795</td>\n",
       "      <td>0.502513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-01</th>\n",
       "      <td>0.542863</td>\n",
       "      <td>0.670017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-01</th>\n",
       "      <td>0.613397</td>\n",
       "      <td>0.832496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-05-01</th>\n",
       "      <td>0.731775</td>\n",
       "      <td>0.914573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ip  sentiment\n",
       "DATE                           \n",
       "1981-01-01  0.526669   0.576214\n",
       "1981-02-01  0.513795   0.502513\n",
       "1981-03-01  0.542863   0.670017\n",
       "1981-04-01  0.613397   0.832496\n",
       "1981-05-01  0.731775   0.914573"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we split our data into a train and a test set, using the last 24 months to test the out-of-sample performance, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.901297Z",
     "start_time": "2020-06-22T00:52:01.895180Z"
    }
   },
   "outputs": [],
   "source": [
    "test_size =24\n",
    "train_size = X.shape[0]-test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.909890Z",
     "start_time": "2020-06-22T00:52:01.902583Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, y_train = X[:train_size], y[:train_size]\n",
    "X_test, y_test = X[train_size:], y[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.920371Z",
     "start_time": "2020-06-22T00:52:01.911206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((426, 18, 2), (24, 18, 2))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a similar architecture with two stacked LSTM layers with 12 and 6 units, respectively, followed by a fully-connected layer with 10 units. The output layer has two units, one for each time series. We compile them using mean absolute loss and the recommended RMSProp optimizer, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.930691Z",
     "start_time": "2020-06-22T00:52:01.921637Z"
    }
   },
   "outputs": [],
   "source": [
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.937094Z",
     "start_time": "2020-06-22T00:52:01.932097Z"
    }
   },
   "outputs": [],
   "source": [
    "n_features = output_size = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:01.949861Z",
     "start_time": "2020-06-22T00:52:01.938235Z"
    }
   },
   "outputs": [],
   "source": [
    "lstm_units = 12\n",
    "dense_units = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:03.145725Z",
     "start_time": "2020-06-22T00:52:01.953031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer LSTM will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
     ]
    }
   ],
   "source": [
    "rnn = Sequential([\n",
    "    LSTM(units=lstm_units,\n",
    "         dropout=.1,\n",
    "         recurrent_dropout=.1,\n",
    "         input_shape=(window_size, n_features), name='LSTM',\n",
    "         return_sequences=False),\n",
    "    Dense(dense_units, name='FC'),\n",
    "    Dense(output_size, name='Output')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has 1,268 parameters, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:03.160171Z",
     "start_time": "2020-06-22T00:52:03.146906Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "LSTM (LSTM)                  (None, 12)                720       \n",
      "_________________________________________________________________\n",
      "FC (Dense)                   (None, 6)                 78        \n",
      "_________________________________________________________________\n",
      "Output (Dense)               (None, 2)                 14        \n",
      "=================================================================\n",
      "Total params: 812\n",
      "Trainable params: 812\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "rnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:03.187168Z",
     "start_time": "2020-06-22T00:52:03.162086Z"
    }
   },
   "outputs": [],
   "source": [
    "rnn.compile(loss='mae', optimizer='RMSProp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train for 50 epochs with a batch_size value of 20 using early stopping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:03.191248Z",
     "start_time": "2020-06-22T00:52:03.188183Z"
    }
   },
   "outputs": [],
   "source": [
    "lstm_path = (results_path / 'lstm.h5').as_posix()\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=lstm_path,\n",
    "                               verbose=1,\n",
    "                               monitor='val_loss',\n",
    "                               mode='min',\n",
    "                               save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:03.199226Z",
     "start_time": "2020-06-22T00:52:03.192779Z"
    }
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', \n",
    "                              patience=10,\n",
    "                              restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:41.513969Z",
     "start_time": "2020-06-22T00:52:03.200563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.3360\n",
      "Epoch 00001: val_loss improved from inf to 0.18271, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 3s 139ms/step - loss: 0.3360 - val_loss: 0.1827\n",
      "Epoch 2/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1530\n",
      "Epoch 00002: val_loss improved from 0.18271 to 0.04228, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 3s 126ms/step - loss: 0.1530 - val_loss: 0.0423\n",
      "Epoch 3/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0992\n",
      "Epoch 00003: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 128ms/step - loss: 0.0992 - val_loss: 0.0512\n",
      "Epoch 4/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0975\n",
      "Epoch 00004: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 131ms/step - loss: 0.0975 - val_loss: 0.0472\n",
      "Epoch 5/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0991\n",
      "Epoch 00005: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 128ms/step - loss: 0.0991 - val_loss: 0.0487\n",
      "Epoch 6/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0966\n",
      "Epoch 00006: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 118ms/step - loss: 0.0966 - val_loss: 0.0540\n",
      "Epoch 7/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0958\n",
      "Epoch 00007: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 2s 109ms/step - loss: 0.0958 - val_loss: 0.0491\n",
      "Epoch 8/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0920\n",
      "Epoch 00008: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 141ms/step - loss: 0.0920 - val_loss: 0.0462\n",
      "Epoch 9/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0937\n",
      "Epoch 00009: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 135ms/step - loss: 0.0937 - val_loss: 0.0436\n",
      "Epoch 10/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0931\n",
      "Epoch 00010: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 136ms/step - loss: 0.0931 - val_loss: 0.0442\n",
      "Epoch 11/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0888\n",
      "Epoch 00011: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 133ms/step - loss: 0.0888 - val_loss: 0.0445\n",
      "Epoch 12/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0886\n",
      "Epoch 00012: val_loss did not improve from 0.04228\n",
      "22/22 [==============================] - 3s 130ms/step - loss: 0.0886 - val_loss: 0.0443\n"
     ]
    }
   ],
   "source": [
    "result = rnn.fit(X_train,\n",
    "                 y_train,\n",
    "                 epochs=100,\n",
    "                 batch_size=20,\n",
    "                 shuffle=False,\n",
    "                 validation_data=(X_test, y_test),\n",
    "                 callbacks=[early_stopping, checkpointer],\n",
    "                 verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training stops early after 22 epochs, yielding a test MAE of 1.71, which compares favorably to the test MAE for the VAR model of 1.91."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the two results are not fully comparable because the RNN model produces 24 one-step-ahead forecasts, whereas the VAR model uses its own predictions as input for its out-of-sample forecast. You may want to tweak the VAR setup to obtain comparable forecasts and compare their performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:41.803038Z",
     "start_time": "2020-06-22T00:52:41.515533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD6CAYAAABamQdMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3xU9Z3/8dc5c8skkxsQwiUkhEA0XmNC7eNXoa3KRdmfl1VoAhbtA+o+3NXtVl1bcCul1AYs4rp1FVdbW8VaadHa0l+LLsKWilsrgYjBgIgQCEgIMSSZZO7n/P44yZCEJEPCJJM583k+HuOZc/me8/2CvM+Z77kpuq7rCCGEMBU11hUQQggRfRLuQghhQhLuQghhQhLuQghhQhLuQghhQhLuQghhQtZIC2iaxsqVKzlw4AB2u51HH32UvLy88Pw333yT5557DkVRKCsrY8GCBQDceuutpKamApCTk8Pq1auHqAlCCCF6ihjuW7duxe/3s3HjRqqqqlizZg3r168HIBQKsW7dOl577TWSk5OZN28e119/PSkpKQBs2LBhaGsvhBCiVxHDvbKykpkzZwJQXFxMdXV1eJ7FYuGPf/wjVquVxsZGAFJSUti/fz8ej4clS5YQDAZ54IEHKC4u7nc7VVVVOByOQTXC5/MNuuxIZ+a2gbnbJ22LX/HUPp/P12u+Ruxzd7vduFyu8LjFYiEYDIbHrVYrb731FrfccgvTp0/HarWSlJTE0qVL+dnPfsYPfvAD/vVf/7VbmWgz8022Zm4bmLt90rb4FU/t62snFPHI3eVy0dbWFh7XNA2rtXuxOXPmMGvWLJYtW8Ybb7zBTTfdRF5eHoqikJ+fT0ZGBg0NDYwfP77fChYVFZ1ve7qpqakZdNmRzsxtA3O3T9oWv+KpfTU1Nb1Oj3jkXlJSwo4dOwCj66SwsDA8z+128/Wvfx2/34+qqjidTlRVZdOmTaxZswaA+vp63G43WVlZ0WiHEEKI8xDxyH327Nns3LmT8vJydF2noqKCzZs3097eTllZGTfddBN33HEHVquViy66iJtvvplQKMTy5ctZuHAhiqJQUVFxztG+EEKIoRMxcVVVZdWqVd2mFRQUhL+XlZVRVlbWbb7FYmHdunVRqqIQIhEEAgHq6urwer2xrgqBQKDP7o5YSUpKIicnB5vNdl7Ly+G0EGJEqKurIzU1lcmTJ6MoSkzr4vF4cDqdMa1DV7qu09jYSF1dHfn5+edVRu5QFUKMCF6vl9GjR8c82EciRVEYPXr0gH7VSLgLIUYMCfa+DfTPJu7D/aMTLdScin0fnRBCjCRxH+4/e+cwP/7LqVhXQwhhAq+//jqPP/54rKsRFXEf7kXjUznpDnKqVY7ehRCiU9xfLVOSlwnA7toz3HDZuBjXRggRDa9V1vHrXceius6vTZ/E7aU557XsSy+9xFtvvYXVamX69Ok89NBDVFZW8thjj2G1WklLS+Pxxx+noaGB5cuXY7VasVgs/PjHPyY7Ozuq9R6suA/3SyekYVVh99EmCXchxAWrra3lxIkTvPrqq1itVv75n/+Z7du387e//Y3Zs2ezdOlStm3bRktLC++++y6XXnopy5YtY9euXTQ3N0u4R4vDaqFwtIPdtU2xrooQIkpuL80576PsaKupqWHGjBnhm4WmT5/OwYMHueeee3j22We56667yM7O5oorrmD+/Pk8//zzfPOb3yQ1NZX7778/JnXuTdz3uQNcnJXE3uPN+INarKsihIhzRUVFVFdXEwwG0XWd999/n/z8fDZv3szf//3fs2HDBqZNm8avf/1r3n77bUpLS3nxxRe54YYb+OlPfxrr6ofF/ZE7wCVjk3j9o2b2nWjmqtzMWFdHCBHH8vLyuPzyy1m4cCGaplFaWsqsWbPYu3cvy5YtIzk5GZvNxqpVq9B1nYceeoinnnoKVVVZvnx5rKsfZopwL8oynmdcWdsk4S6EGLTbbrsNMB4/8A//8A/d5l155ZW8/vrr55TZuHHjsNRtoEzRLTMq2UpOppM9R8/EuipCCDEimCLcAUpyM6mUk6pCCAGYKNxL8zI52eLlxBlPrKsihBAxZ5pwL+noa5ejdyGEMFG4Xzw+FafNwu6jEu5CCGGacLdZVK7ISZebmYQQAhOFOxj97vtOtOANhGJdFSGEiClThXtJbiZBTWdvXXOsqyKEMKnFixdz6NChPudfd911+Hy+YaxR70xxE1On8BMijzZxdf6oGNdGCDFoVb+CPS9Hd51XfR2KF0Z3nSOYqcJ9VIqd/DEpcsWMEGLA7rvvPu68806uvvpqqqur+clPfsKoUaNobW2lqamJBQsWsGjRovNeX11dHf/2b/9GMBhEURS+973vcfHFF7Ns2TKOHj2Kz+dj6dKlzJs3j3//93/nr3/9K5qm8Xd/93d84xvfuOD2mCrcweia+Z8Dp9B1Xd7HKES8Kl447EfZCxYs4Le//S1XX301v//97/niF79IYWEhc+bMob6+nsWLFw8o3H/84x+zePFiZs2aRU1NDQ8//DAvvfQS7733Hq+99hoAO3fuBOCNN97g5ZdfJjs7u9dHHAyG+cI9L4PXdtdx9PN28kanxLo6Qog4MXPmTNauXcuZM2fYs2cPP/vZz1i3bh1vvfUWLpeLYDA4oPUdOnSIL3zhC4DxpMmTJ0/icrl45JFHeOSRR3C73dx8880APPHEEzzxxBOcPn2amTNnRqU9pjqhCsYVMyA3MwkhBkZVVW644QZWrlzJtddeywsvvEBxcTGPP/44N9xwA7quD2h9BQUF7Nq1CzCeET9mzBhOnTrFvn37ePrpp3nuuedYu3Ytfr+fLVu28MQTT/Diiy/y29/+luPHj19we0x35D5tbCqpDiu7jzZxW0lsHvYvhIhPt99+O7NmzeJ3v/sdp0+fZuXKlWzevJmMjAwsFgt+v/+81/Wd73yHRx55hBdeeIFgMMiPfvQjsrKyaGho4NZbbyU5OZklS5Zgt9tJT0/nlltuIT09nWuuuYYJEyZccFsihrumaaxcuZIDBw5gt9t59NFHycvLC89/8803ee6551AUhbKyMhYsWBCxzFCyqArFuRlU1soTIoUQAzN+/Hj27duHx+Nh6tSpbNmy5ZxlNmzY0O86tm3bBkBOTg4///nPz5m/atWqc6bdd9993HfffYOsde8ihvvWrVvx+/1s3LiRqqoq1qxZw/r16wEIhUKsW7eO1157jeTkZObNm8f111/Prl27+iwzHEpyM3lq20HcviAuh+l+nAghYmzv3r2sXbv2nOk33njjgE66DqWIyVdZWRnu4C8uLqa6ujo8z2Kx8Mc//hGr1UpjYyMAKSkp/Zbpi8/no6amZlCN8Hq93cpmKe1oOvx+516uGu8c1DpHip5tMxszt0/aNjCBQID29vYRcZWbrut4PH0/YXbatGk899xzvc7rr9yF1ikQCJz3n3vEcHe73bhcrvC4xWIhGAxitRpFrVYrb731FqtWreIrX/kKVqs1YpneOBwOioqKzqvSPdXU1HQrO2FygEfePkmjnkpR0bRBrXOk6Nk2szFz+6RtA3P48GHa29sZPXp0zAPe4/HgdI6cA0Nd12lsbCQ1NZX8/Pxu8/oK+4jh7nK5aGtrC49rmnZOSM+ZM4dZs2axbNky3njjjfMqM5TSnTamjXVRKU+IFCJu5OTkUFdXR0NDQ6yrQiAQwGazxboa3SQlJZGTc/4XiURM3JKSErZv3868efOoqqqisLAwPM/tdnPPPffwwgsvYLfbcTqdqKrab5nhUpqXyR8/PImm6ahq7H/mCSH6Z7PZzjkqjRUz/OqKGO6zZ89m586dlJeXo+s6FRUVbN68mfb2dsrKyrjpppu44447sFqtXHTRRdx8880oinJOmeF2VW4mv/rbMT497Wbq2NRh374QQsRSxHBXVfWcS3cKCgrC38vKyigrKzunXG+X+wynzpuZdteekXAXQiQc092h2mnKmBQykm1yp6oQIiGZNtwVRaEkN1NeuyeESEimDXeAktwMDp5y09weiHVVhBBiWJk73Dv63fcck6N3IURiMXW4X5mTgaogL80WQiQcU4d7isNK0fg0uZlJCJFwTB3uYDxErOroGULawJ7FLIQQ8cz04V6al0mbP8SBk62xrooQQgwb04d7SW7HzUzSNSOESCCmD/dJo5yMcTnkpKoQIqGYPtyNm5ky5MhdCJFQTB/uYPS7H2ls57TbF+uqCCHEsEiIcA/fzHRU3qsqhEgMCRHul09Mx2ZR5CFiQoiEkRDhnmSzcMmEdOl3F0IkjIQId4DS3Ez21p0hENJiXRUhhBhyCRPuJXkZeAMaNZ+1xLoqQggx5BIm3DvfzCT97kKIRJAw4T4+3cmE9CR2yxUzQogEkDDhDnBVXqbcqSqESAgJFe6luZkcP+PhZLM31lURQoghlVDh3nkzk1wSKYQwu4QK90vGp+GwqnJSVQhhegkV7naryhU5cjOTEML8EircweiaqT7ejDcQinVVhBBiyCReuOdmEgjp7DvRHOuqCCHEkLFGWkDTNFauXMmBAwew2+08+uij5OXlhef/4Q9/4MUXX8RisVBYWMjKlStRVZVbb72V1NRUAHJycli9evXQtWIAOt/MVFnbRGneqBjXRgghhkbEcN+6dSt+v5+NGzdSVVXFmjVrWL9+PQBer5cnn3ySzZs343Q6eeCBB9i+fTszZswAYMOGDUNb+0HISnWQOyqZ3bVyM5MQwrwihntlZSUzZ84EoLi4mOrq6vA8u93Oq6++itPpBCAYDOJwONi/fz8ej4clS5YQDAZ54IEHKC4u7nc7Pp+PmpqaQTXC6/UOqOzUDJX3Pm3go48+QlGUQW1zuAy0bfHGzO2TtsUvM7QvYri73W5cLld43GKxEAwGsVqtqKrKmDFjAOMovb29nWuuuYaPP/6YpUuXsmDBAo4cOcLdd9/Nli1bsFr73pzD4aCoqGhQjaipqRlQ2WvPONn26T5Sx01m0qjkQW1zuAy0bfHGzO2TtsWveGpfXzuhiOHucrloa2sLj2ua1i2kNU1j7dq1HD58mKeeegpFUcjPzycvLy/8PSMjg4aGBsaPHx+Fply4rjczjfRwF0KIwYh4tUxJSQk7duwAoKqqisLCwm7zV6xYgc/n45lnngl3z2zatIk1a9YAUF9fj9vtJisrK9p1H7SLslNJtlvkOTNCCNOKeOQ+e/Zsdu7cSXl5ObquU1FRwebNm2lvb+eyyy5j06ZNTJ8+nbvuuguAO++8k/nz57N8+XIWLlyIoihUVFT02yUz3KwWleJJGVTKzUxCCJOKmLiqqrJq1apu0woKCsLf9+/f32u5devWXWDVhlZJbibr/3yIdn+QZPvI2fEIIUQ0JNxNTJ1K8zIJaTofHJObmYQQ5pOw4X5VbgYgT4gUQphTwoZ7RrKdgqwUOakqhDClhA13MPrddx9tQtf1WFdFCCGiKqHDvTQvk6b2AIdPt0VeWAgh4khCh3vnzUzy8g4hhNkkdLhPzXKRmmRl91F5iJgQwlwSOtxVVeGq3Ew5qSqEMJ2EDneA0txMPj7VSos3EOuqCCFE1CR8uJfkZaDrUCVdM0IIE0n4cC+elIGiyM1MQghzSfhwT02ycVF2qlwxI4QwlYQPdzAuiaw6egZNk5uZhBDmIOGOcVK11Rfk4Cl3rKsihBBRIeFO9zczCSGEGUi4A5NHJzMqxS797kII05BwBxRFoSQ3Q47chRCmIeHeoSQvk08b2mhq88e6KkIIccEk3DuU5Br97nuOydG7ECL+Sbh3uDInA4uqSL+7EMIUJNw7OO0WLhmfxu5aeQyBECL+Sbh3UZqXSdWxMwRDWqyrIoQQF0TCvYurcjPwBELsP9ka66oIIcQFkXDvolRuZhJCmISEexcTM5yMTXXISVUhRNyzRlpA0zRWrlzJgQMHsNvtPProo+Tl5YXn/+EPf+DFF1/EYrFQWFjIypUrAfotM1IpikJpXqYcuQsh4l7EI/etW7fi9/vZuHEjDz74IGvWrAnP83q9PPnkk7z00ku8+uqruN1utm/f3m+Zka4kN5Njn3s41eqNdVWEEGLQIoZ7ZWUlM2fOBKC4uJjq6urwPLvdzquvvorT6QQgGAzicDj6LTPShR8iJpdECiHiWMRuGbfbjcvlCo9bLBaCwSBWqxVVVRkzZgwAGzZsoL29nWuuuYY//elPfZbpi8/no6amZlCN8Hq9gy7bkzWkY1Xhv/d8Qp4l9t0z0WzbSGTm9knb4pcZ2hcx3F0uF21tbeFxTdO6hbSmaaxdu5bDhw/z1FNPoShKxDK9cTgcFBUVDaYN1NTUDLpsb67IaaLWrUR1nYMV7baNNGZun7QtfsVT+/raCUXslikpKWHHjh0AVFVVUVhY2G3+ihUr8Pl8PPPMM+HumUhlRrqS3Ez2Hm/GH5SbmYQQ8Snikfvs2bPZuXMn5eXl6LpORUUFmzdvpr29ncsuu4xNmzYxffp07rrrLgDuvPPOXsvEk9K8TH76zmH2nWjmqo4HigkhRDyJGO6qqrJq1apu0woKCsLf9+/f32u5nmXiSedJ1craJgl3IURckpuYepGdlsTEDCd7jsoVM0KI+CTh3oeSvEy5U1UIEbck3PtQmpvByRYvJ854Yl0VIYQYMAn3PnTtdxdCiHgj4d6HovFpJNlUec6MECIuxX+4t57E2vZZ1Fdrs6hckZPBbjlyF0LEofgP922Pkrvj/iFZdWleJvtOtOANhIZk/UIIMVTiP9zHXYGj5Qg0Hor6qktzMwlqOnvrmqO+biGEGErxH+6Fc4zhx29GfdVX5WYAclJVCBF/4j/cMyfjS5sMB6Mf7qNdDvLHpMhJVSFE3In/cAfc46+BIzvBF/0XW1+Va5xU1XU96usWQoihYo5wn/Al0ALw6f9Efd2leZk0tvk5+nl71NcthBBDxRTh3j7mSnCkD0m/e0mu3MwkhIg/pgh3VCsUXAsH/xui3H1SmJ2Ky2GVfnchRFwxR7gDFM4F90k4uTeqq7WoCsWTMqiUd6oKIeKIecJ96mxAgY/fivqqS/IyOXCyBbcvGPV1CyHEUDBPuLuyYGLJkFwSWZKbgabDB8fk6F0IER/ME+4A0+ZC3S5oOx3V1Xa+jUmeMyOEiBfmCvfCOYAOn2yN6mrTnTamjXVRKSdVhRBxwlzhPu5KcGUPySWRpXmZ7Dl6Bk2Tm5mEECOfucJdVY0Tq4fehlB0T36W5GbS7Anw6Wl3VNcrhBBDwVzhDkbXjLcZjr0X1dV2vplpt1wSKYSIA+YL9ynXgmqL+lUzU8akkO60yZ2qQoi4YL5wT0qDvP8T9evdVVWhJDdDTqoKIeKC+cIdjEsiG2rgzNGorrYkN5NPTrlpbg9Edb1CCBFt5gz3wrnGMMpXzZR29rsfk6N3IcTIFjHcNU1jxYoVlJWVsXjxYmpra89ZxuPxUF5ezqFDZ191d+utt7J48WIWL17M8uXLo1vrSEZPhcx8OBjdrpkrJ2XgtFl4fsenhOSSSCHECGaNtMDWrVvx+/1s3LiRqqoq1qxZw/r168PzP/zwQ77//e9TX18fnubz+QDYsGHDEFT5PCiKcfRe+Qvwt4M9OSqrTXFY+cEtl/KdTXt5cuvHPDjnoqisVwghoi1iuFdWVjJz5kwAiouLqa6u7jbf7/fz9NNP853vfCc8bf/+/Xg8HpYsWUIwGOSBBx6guLi43+34fD5qamoG0wa8Xu85ZVOSisgNejn2l1dwT7hmUOvtzeUpMHuqi6e2fUKW4uYLOdHZcfSlt7aZiZnbJ22LX2ZoX8Rwd7vduFyu8LjFYiEYDGK1GkVLS0vPKZOUlMTSpUtZsGABR44c4e6772bLli3hMr1xOBwUFRUNpg3U1NScW3baFHj3YSZ5PoKibw5qvX35SUEhf//MTp7430b+37cuYWKGM6rr76rXtpmImdsnbYtf8dS+vnZCEfvcXS4XbW1t4XFN0/oNaYD8/HxuvvlmFEUhPz+fjIwMGhoaBljlC2R1wJSvGv3uUX6Bh9NuYf3XSwmGdO795W78QS2q6xdCiAsVMdxLSkrYsWMHAFVVVRQWFkZc6aZNm1izZg0A9fX1uN1usrKyLrCqg1A4B5qPwano/7zKH5PC2vlXUHXsDBV/jO+fb0II84nYLTN79mx27txJeXk5uq5TUVHB5s2baW9vp6ysrNcy8+fPZ/ny5SxcuBBFUaioqIh4tD8kps0xhgffhOxLor76Gy8fz5Jr8nlh52GmT87k/14xIerbEEKIwYiYuKqqsmrVqm7TCgoKzlmu65UxdruddevWRaF6FyhtAoy73Lhbdcb9Q7KJZTdeTNWxJr67aS9F49MoyHJFLiSEEEPMnDcxdTVtrvEQMc/Q3Hhkt6r856IS7FaVf3p5Nx5/aEi2I4QQA2H+cC+cC3oIPnl7yDYxIcPJk+VX8fGpVv7tjQ/Ro3wCVwghBsr84T6xFJyjon63ak9fKcziW9dN4/Xdx9n4/rEh3ZYQQkRi/nBXLTB1lvHqPW1ou0y+df00Zkwdw4rf72PfieYh3ZYQQvTH/OEORtdMeyMc3z2km7GoCv9RXsyoZDv/9MvdNHvk6ZFCiNhIjHAvuA4UNeov8OjNaJeD/1x0FcebPDz0mw+k/10IEROJEe7Jo2DSF4fkxdm9mT55FMtuvJi3PqrnZ+8cHpZtCiFEV4kR7mDc0HRyL7R8NiybWzojn7mXZrP6T/t5/8jnw7JNIYTolDjh3vkCjyG+aqaToiisXXAlOZlO7ntlN6fdvmHZrhBCQCKF+9hLIC1n2MIdIC3JxjN3lNDUHuDbr1bJCz6EEMMmccJdUYwHiR3aDsHhO4q+dEI6P7zlUt755DT/8fbBYduuECKxJU64g/EogkAb1O4c1s1+bfok5pfm8NS2g/zPgVPDum0hRGJKrHDP/zJYk4wHiQ0jRVH44S2XcVF2KvdvrOLEGc+wbl8IkXgSK9ztyTB55rBc796T027hmTtKCIR07n1FXvAhhBhaiRXuYFw18/mncPqTYd/0lCwXj91+BXuOnmH1n+QFH0KIoZN44d71BR4x8HdXjOcbX5rMz3ce4f/tHZ5r7oUQiSfxwj0zD7IuHra7VXvz8Lwiiidl8N3X9vJpgztm9RBCmFfihTsYR++174KvNSabt1tVnr6jBJtF4Z9+KS/4EEJEX2KGe+Fc0ALGNe8xMjHDyb+XFXOgvpVHflcds3oIIcwpMcN90hfBkR6zfvdOX71oLP987VQ2Vdbxa3nBhxAiihIz3C02mHodHPxv0GJ7SeK/zCrkmqmjeeR31fKCDyFE1CRmuIPR7+6uh5MfxLQaxgs+riIj2cY//XI3LV55wYcQ4sIlbrhPnQ0ow363am/GuBz856IS6po8fOc3e+UFH0KIC5a44e7KgoklMe937/SFyaNYdsPFbNl3Ul7wIYS4YIkb7mA8SOz4bnA3xLomAHxzZj5zLslmzZ/2U1krL/gQQgxexHDXNI0VK1ZQVlbG4sWLqa2tPWcZj8dDeXk5hw4dOu8yI0LhHECHT/471jUBzr7gY0KGk3t/uYdGecGHEGKQIob71q1b8fv9bNy4kQcffJA1a9Z0m//hhx9yxx13cOzYsfMuM2KMuxJc2TG9W7WndKfxgo/P2/18e6O84EMIMTgRw72yspKZM2cCUFxcTHV19xtu/H4/Tz/9NFOmTDnvMiOGqsK02cbNTKGRc5XKZRPT+cHNl/KXg6d5aU8Txz5vp9Htw+MPmeJkq6bpePwhPm/z4w1opmiTECONNdICbrcbl8sVHrdYLASDQaxWo2hpaemAy/TG5/NRUzO4JyV6vd5Bl01NvpQc38vUvvNr2seWDGodQ+FKl871BS5+XX2GX1efvZNWARxWBYdVIcmqktRl6DhnvJ9lbB3jlo55NmOoAL6Qji+o4Q3q+ILGd19I7xjXOubreINax3wdX0jrmNZ1+XPnG9+7h7mqHMFlV0m2qbjsKildPzYVl91Csl3tfRmbMbSoyvD+BZ2HC/n/cqQzc9vAHO2LGO4ul4u2trbwuKZp/Yb0YMs4HA6KiooiVadXNTU1gy7LlBz43xXk+fZD0R2DW8cQWV+oseHt3aSOzsbjD9HuD+HxB2n3h2gPhPD4jY/xPUibP8Rpd4h2f4B2fxBPIEQgNPRHxXaLSpJNxWm34LRZSLJZcNotZDiN8c7pPec7rCrHTpzEmZZJiydIizdAqzdIiydAozfI4WZj3O0LRqxDit1CapKNNKfVGCZZe4zbcCVZsakKNouK1dIx7DJuVVVsFgVrj+k2tWN+l+82i4rN0v9O5YL+vxzhzNw2iK/29bUTihjuJSUlbN++nXnz5lFVVUVhYWHEjQ2mTMw4UiHvS8aLs+f8MNa16cZuVflSbgpFRZMGvY5ASMPTsSNo94eM0O/cUfQyXdMh2W4hqTOQbRZjvGtI2ywk2dVwUNssg7/oqqbGS1HRxf0uEwxpuH1BWr1Bmj0dO4AuO4Kz44HwTuK028+np9vCywSH6NyFonA2/HvuKAgycWczWS4HY1wOslKNT+f3MS4Ho1LsI/JXh4h/EcN99uzZ7Ny5k/LycnRdp6Kigs2bN9Pe3k5ZWdl5lxnRCufCmw9DU63xSGAT6TzCTEuyxboqg2a1qGQk28lItjOY3Zyu63gDGq2+AIGQTjCkGUNNIxjSCYQ0glrHsGO6sZzx3R805nct13V+5zq7riOgaXzW0ES7P0Tl0SYaWn14A+c+6kJVYLSra+DbjZ1A587A5WBMxzAj2YaiDN2OQNd1/CENr1+jPWDs7D2BEN5A56/Gs+O1dc1UNtdiURUsqrFjM4ZqL9M6hhYFVTm7jNVydr6qdB1Xu5Xr/D6UbTejiOGuqiqrVq3qNq2goOCc5TZs2NBvmRFtWke4H3wLrr471rURUaYoivGrw24Z1u12/Wmv6zpt/hANrT5Ou300tPq6fe8cHjrlpqHVhz907o7Aqirn7AQ6x0e7HAB4/J2h3PmLLdgx1PB0CWxPQOs2zwjwIAP7gdMYhT+l82O3qmSnORiXlsTYtCTGdXzGdkzLTktiXHoSSbbh/TseySKGe0IYMxVGTTEuiZRwF0NAURRcDisuh83/W9sAAA6QSURBVJX8MSn9LqvrOi3eYP87ArePms9aOe329dvl1Ns5D6fNQobTxvi0JJwdXW7JXZZJ6qM7rut47aefMHXqNIKaTqjjEwwPNTQNgprWbXr35bQ+yupoPZcJ6bT6gtS3eDnZ7OWjEy1sqzmFJ3DuexDSkqyMSzfCPrtjB5Cd5jg7np7EGJcjIbrCJNw7TZsLlT8Hf7vxIm0hYkRRFNKdNtKdNqaOdfW7rKbpNHsCnHb7zv5C6Qhih1Udsq6MFqeVsWlJQ7Lu86HrHYHf7KW+xcfJFi/1HZ+TzV7qW30crD9Ng9t3zr0iqgJZqY4+dwDZaUmcaAmQ0thO1z8+RTH+bpTO7ygdQzr+031az2VROLt8l/l2q3pB5636IuHeqXAOvLceDu+Ai26IdW2EOC+qqpCZYiczxR7rqgwrRVFI67gKalp2ap/LhTSdRrcvvAM42eLlVJcdwNHGdt4/8jln2nu7z2V43rGQmWzj3WXXR73bUMK9U941YEsxHiQm4S6EKVhUhbEd/fSXk97nct5AiFNdfgEcOVbH+PETAONXgg6gg46OroMOHUNjvGM2dCyr62fLnV3+7C+IrmXHpjlwWOXIfehYHVBwrfEIYF0HOTMvRMJIslnIHZ1M7mijS7bG3kxRUU6Ma3VhEvupkD1Nmw0tdXDqo1jXRAghLoiEe1fT5hjDEfQgMSGEGAwJ967SJsC4y43r3YUQIo5JuPc0bS4cew/a5WUZQoj4JeHeU+Fc0DU4tC3WNRFCiEGTcO9pYikkj5Z+dyFEXJNw70m1wNRZ8MlW0M69vVkIIeKBhHtvps0Bz+dQtyvWNRFCiEGRcO/N1OtBsRh3qwohRByScO+NMxMmfdG4W1UIIeKQhHtfCudA/YfQfDzWNRFCiAGTcO/LtLnGUG5oEkLEIQn3vowtgvRJEu5CiLgk4d4XRTGumvn0fyDoi3VthBBiQCTc+1M4FwLtcOSdWNdECCEGRMK9P5NngjVJumaEEHFHwr0/9mTI/7LxKAJ9QK+FF0KImJJwj2TaHGg6DI2fxLomQghx3iTcI5EXeAgh4pCEeySZeZB1sTyKQAgRV+QF2edj2hz46zPgbYGktFjXRkSi6+BpgqYjcKYWmmqNYXsjuMZBxiRIzzHuY0jPgZSxoMpxjjCXiOGuaRorV67kwIED2O12Hn30UfLy8sLzt23bxtNPP43VauX222/na1/7GgC33norqampAOTk5LB69eohasIwKJwL7/4EPt0Ol9wS69oIAJ+7e3B3Gx4Ff2v35Z2ZkDwGPtl27jyLHdImdg/8rjuAtInGyXUh4kjEcN+6dSt+v5+NGzdSVVXFmjVrWL9+PQCBQIDVq1ezadMmnE4nCxcu5NprryUtzTi63bBhw9DWfrhM+iI40o0HiQ11uAf9UF9tPG74+C4m11XD7hxwjYXUceDKPvtJ7RjanENbp1gI+uDMMThz5Gxwnzna/Si8K1uK0YWWkQuTZ3R8zzs7LSn97LLeZmPdzXXQ3GN4+M/Q+pnxNq6uksd0hH1H4IfDv2M8Jcu48U2IESJiuFdWVjJz5kwAiouLqa6uDs87dOgQubm5pKcb/3BKS0vZtWsXEyZMwOPxsGTJEoLBIA888ADFxcVD1IRhYLHB1OuM6901LXo/4XXdCKzju4wwr9sFn30AoY47Yl3ZaMmTjLD5rAraGs4NHTB2PKnZvQd/eHyccfQaywDSdQj5IeCBoBf8bSSfqgRvZffgbqo12kyXy09VmxGoGXlQdFOX4J5sDJNHn3/bktJhXDqMu6z3+aEAtJzoCP06aD569nvjJ3BoOwTaupexOLqHfXoOGW2AdqDjV8MoY+jMBFuy7AjEkIsY7m63G5fLFR63WCwEg0GsVitutzvc9QKQkpKC2+0mKSmJpUuXsmDBAo4cOcLdd9/Nli1bsFr73pzP56OmpmZQjfB6vYMue77SUy5nQttvOfy/b+AdVTSodaiBNpI+/whnYzXOxn04G/dh9TUBoFkceDMvwlNwG54xl+EZdSnB5Gy8Ph9JSUnGCrQQFt8ZrN5GrN7TWD2fG0NvI1ZPI1Z3I9bTh7F6TqOGvOdsX1etBJNGd/84O4djCCaNQrckoYR8qCFft6ES8qEGvb3OO2fYsVyv66H7/QKdHXw6CsHksfhTJhAYXUwgdx6BlAn4XRMIpEwgmDTaeEtWT61AawPQMKi/k/6NAvsoyLoCsrr+Qeqo/hZs7fXY2k9iazsZ/m5tqcf22T5s3tOMB+jlfS+aakOzpxGypxFypBvDnp8e0zV7Gpp15OwUhuPfXCyZoX0Rw93lctHWdvYoRdO0cEj3nNfW1kZqair5+fnk5eWhKAr5+flkZGTQ0NDA+PHj+9yOw+GgqGhwoVlTUzPosuctNwv+9kPyAx9D0W2Rlw8FoaHm7BH58V3QcIDw0ejoaXDxDcY7W3O+gJp9KckWGz17dgfdNl8ruE9B60lw14O7HsVdj621HlvHOPX7oe00MIgbtCx2sDrBlmTcxWtzGp8kJ9jSu8zrsYw1yThy7ZhX2+Qn74oZKOmTsFnt2AZek5Ep6OPgB39l2sTRxsnd8OdzVE8TqqcJq6cJPGeMt361HjLmB9r7Xqdq7Tj67/IroNsnw/hV0tsnyr8WhuXfXAzFU/v62glFDPeSkhK2b9/OvHnzqKqqorCwMDyvoKCA2tpazpw5Q3JyMrt27WLp0qVs2rSJjz/+mJUrV1JfX4/b7SYrK6ufrcSBlDFGEH/8Jnx12bnzW06cDfG6XXBiz9l/qM5RkDMdLr3NGE4sMf4xDiVHqvEZXdD/cqGAEfDuk9Bab3SXhEPY2T24uwZ1b0fRg9BeUxO5jvHI6iCYPBbGDTAgAp6OwG/q4/P52e8tdcb5GU8T+N39r1e19h384c/w7Ry60XXQgsY7i7Ug6KGz38PTNGO6rnd87+ejRZjfbR2hXuenHT8OwX1GmxUVUDrar5zHNHrMU7sv13OaayyMyo/6H2vEcJ89ezY7d+6kvLwcXdepqKhg8+bNtLe3U1ZWxrJly1i6dCm6rnP77beTnZ3N/PnzWb58OQsXLkRRFCoqKvrtkokbhXNh+4+MS+yaj3fvK289YSyj2mD8FXDVYiPIc6ZDZv6I+Tl9DosN0sYbHxF7nTvSgf59BP3GieLw50yP8V4+LZ+d/R709L/+HjuH3KACf3X2COSgEaxasEdIh/qe1hmuI8zE4dyYYoFlR8HhirzsAERMXFVVWbVqVbdpBQVnj7Suu+46rrvuum7z7XY769ati1IVR5Bpc4xw/48rz07LnAx5X+o4Ip8O4y43jm6FGE5WO7iyjM9gBH3GfRy97Rx8LefsGJQzpwAFrA5Qk43wV63GEalqNX7ZqVYjuDq/nzPN0qVcj2k9yynq2WmdR72K2s9HMZaPuIzaZZ1q+Oj70KeHKJgypeOZUvrZI370PqbRz3Id0/ua5sqOerCD3MQ0MOOvhBn3G//DTew4Kk8ZE+taCXHhrI4B7Rxq46hPejD8p0OQdVGsq3FBJNwHQlFg1spY10IIISKSe66FEMKEJNyFEMKEJNyFEMKEJNyFEMKEJNyFEMKEJNyFEMKEJNyFEMKEJNyFEMKEFF3XB/FIwOirqqrC4XDEuhpCCBFXfD5fr+/LGDHhLoQQInqkW0YIIUxIwl0IIUxIwl0IIUxIwl0IIUxIwl0IIUxIwl0IIUworsNd0zRWrFhBWVkZixcvpra2NtZVippAIMBDDz3EokWLmD9/Pm+//XasqxR1jY2NfOUrX+HQoUOxrkrU/dd//RdlZWXcdttt/OY3v4l1daImEAjw4IMPUl5ezqJFi0zzd/fBBx+wePFiAGpra1m4cCGLFi3i+9//Ppo28t7xej7iOty3bt2K3+9n48aNPPjgg6xZsybWVYqa3//+92RkZPDKK6/w/PPP88Mf/jDWVYqqQCDAihUrSEoy3/tm33vvPfbs2cOvfvUrNmzYwMmTJ2Ndpaj585//TDAY5NVXX+Xee+/lySefjHWVLtjzzz/P9773PXw+HwCrV6/m29/+Nq+88gq6rsftgVVch3tlZSUzZ84EoLi4mOrq6hjXKHpuuOEG/uVf/iU8brFYYlib6HvssccoLy9n7Nixsa5K1L3zzjsUFhZy7733cs899/DVr3411lWKmvz8fEKhEJqm4Xa7sVrj/02dubm5PPXUU+Hxffv2cfXVVwPw5S9/mXfffTdWVbsgcf0343a7cbnOvjXcYrEQDAZN8T9cSkoKYLTxW9/6Ft/+9rdjXKPoef311xk1ahQzZ87kueeei3V1oq6pqYkTJ07w7LPPUldXxz/+4z+yZcsWFEWJddUuWHJyMsePH+fGG2+kqamJZ599NtZVumBz586lrq4uPK7revjvKiUlhdbW1lhV7YLE9ZG7y+Wira0tPK5pmimCvdNnn33GnXfeyS233MJNN90U6+pEzWuvvca7777L4sWLqamp4bvf/S4NDQ2xrlbUZGRkMGPGDOx2O1OmTMHhcPD555/HulpR8Ytf/IIZM2bw5ptv8rvf/Y5ly5aFuzPMQlXPxmJbWxtpaWkxrM3gxXW4l5SUsGPHDsB48FhhYWGMaxQ9p0+fZsmSJTz00EPMnz8/1tWJql/+8pe8/PLLbNiwgaKiIh577DGysrJiXa2oKS0t5S9/+Qu6rlNfX4/H4yEjIyPW1YqKtLQ0UlNTAUhPTycYDBIKhWJcq+i65JJLeO+99wDYsWMH06dPj3GNBieuD3Nnz57Nzp07KS8vR9d1KioqYl2lqHn22WdpaWnhmWee4ZlnngGMEz9mPAFpNtdeey3vv/8+8+fPR9d1VqxYYZpzJt/4xjd4+OGHWbRoEYFAgPvvv5/k5ORYVyuqvvvd7/LII4/wxBNPMGXKFObOnRvrKg2KPBVSCCFMKK67ZYQQQvROwl0IIUxIwl0IIUxIwl0IIUxIwl0IIUxIwl0IIUxIwl0IIUzo/wMyPJE91IpKmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(result.history).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:42.052369Z",
     "start_time": "2020-06-22T00:52:41.804368Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24 entries, 2018-01-01 to 2019-12-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ip         24 non-null     float32\n",
      " 1   sentiment  24 non-null     float32\n",
      "dtypes: float32(2)\n",
      "memory usage: 384.0 bytes\n"
     ]
    }
   ],
   "source": [
    "y_pred = pd.DataFrame(rnn.predict(X_test), \n",
    "                      columns=y_test.columns, \n",
    "                      index=y_test.index)\n",
    "y_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:42.061194Z",
     "start_time": "2020-06-22T00:52:42.053478Z"
    }
   },
   "outputs": [],
   "source": [
    "test_mae = mean_absolute_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:42.082715Z",
     "start_time": "2020-06-22T00:52:42.062836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.042280906448258576\n"
     ]
    }
   ],
   "source": [
    "print(test_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:42.101409Z",
     "start_time": "2020-06-22T00:52:42.084681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',\n",
       "               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',\n",
       "               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',\n",
       "               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n",
       "               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n",
       "               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01'],\n",
       "              dtype='datetime64[ns]', name='DATE', freq=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T00:52:43.952807Z",
     "start_time": "2020-06-22T00:52:42.104061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(17, 4))\n",
    "pd.DataFrame(result.history).rename(columns={'loss': 'Training',\n",
    "                                              'val_loss': 'Validation'}).plot(ax=axes[0], title='Train & Validiation Error')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('MAE')\n",
    "col_dict = {'ip': 'Industrial Production', 'sentiment': 'Sentiment'}\n",
    "\n",
    "for i, col in enumerate(y_test.columns, 1):\n",
    "    y_train.loc['2010':, col].plot(ax=axes[i], label='training', title=col_dict[col])\n",
    "    y_test[col].plot(ax=axes[i], label='out-of-sample')\n",
    "    y_pred[col].plot(ax=axes[i], label='prediction')\n",
    "    axes[i].set_xlabel('')\n",
    "\n",
    "axes[1].set_ylim(.5, .9)\n",
    "axes[1].fill_between(x=y_test.index, y1=0.5, y2=0.9, color='grey', alpha=.5)\n",
    "\n",
    "axes[2].set_ylim(.3, .9)\n",
    "axes[2].fill_between(x=y_test.index, y1=0.3, y2=0.9, color='grey', alpha=.5)\n",
    "\n",
    "plt.legend()\n",
    "fig.suptitle('Multivariate RNN - Results | Test MAE = {:.4f}'.format(test_mae), fontsize=14)\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.85)\n",
    "fig.savefig(results_path / 'multivariate_results', dpi=300);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t-dl]",
   "language": "python",
   "name": "conda-env-ml4t-dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
